mindspore.ops.CombinedNonMaxSuppression

class mindspore.ops.CombinedNonMaxSuppression(pad_per_class=False, clip_boxes=True)[source]

Greedily selects a subset of bounding boxes in descending order of score.

Parameters

  • clip_boxes (bool, optional) – If true, assume the box coordinates are between [0, 1] and clip the output boxes if they fall beyond [0, 1]. If false, do not do clipping and output the box coordinates as it is. Defaults to true.

  • pad_per_class (bool, optional) – If false, the output nmsed boxes, scores and classes are padded/clipped to max_total_size. If true, the output nmsed boxes, scores and classes are padded to be of length max_size_per_class * num_classes, unless it exceeds max_total_size in which case it is clipped to max_total_size. Defaults to false.

Inputs:

  • boxes (Tensor) - A Tensor of type float32 and shape (batch_size, num_boxes, q, 4). If q is 1 then same boxes are used for all classes otherwise, if q is equal to number of classes, class-specific boxes are used.

  • scores (Tensor) - A Tensor of type float32 and shape (batch_size, num_boxes, num_classes) representing a single score corresponding to each box (each row of boxes).

  • max_output_size_per_class (Tensor) - A 0D Tensor of type int32, representing the max number of boxes to be selected by non max suppression per class.

  • max_total_size (Tensor) - A 0D Tensor of type int32, representing the maximum number of boxes retained over all classes.

  • iou_threshold (Tensor) - A 0D float32 tensor representing the threshold for deciding whether boxes overlap too much with respect to IOU, and iou_threshold must be equal or greater than 0 and be equal or smaller than 1.

  • score_threshold (Tensor) - A 0D float32 tensor representing the threshold for deciding when to remove boxes based on score.

Outputs:

  • nmsed_boxes - A Tensor of float32 with shape of (batch_size, num_detection, 4), which contains the non-max suppressed boxes.

  • nmsed_scores - A Tensor of float32 with shape of (batch_size, num_detection), which contains score of boxes.

  • nmsed_classes - A Tensor of float32 with shape of (batch_size, num_detection), which contains classes of boxes.

  • valid_detections A Tensor of int32 with shape of (batch_size,), which indicates the number of valid detections of each batch.

Raises

  • TypeError – If the dtype of boxes, scores , iou_threshold , score threshold are not float32.

  • TypeError – If the dtype of max_output_size_per_class and max_total_size are not int32.

  • ValueError – If boxes is not 4D.

  • ValueError – If max_output_size_per_class, max_total_size, iou_threshold and score threshold are not 0D.

  • ValueError – If scores is not 3D.

  • ValueError – If shape[0] or shape[1] of boxes is not same with that of the scores.

  • ValueError – If shape[2] of boxes is not same with shape[2] of scores or 1

  • ValueError – If max_total_size < 0.

  • ValueError – If max_output_size_per_class < 0.

  • ValueError – If iou_threshold not in [0,1].

Supported Platforms:

Ascend CPU

Examples

>>> boxes = Tensor(np.array([[[[200, 100, 150, 100]],
...                           [[220, 120, 150, 100]],
...                           [[190, 110, 150, 100]],
...                           [[210, 112, 150, 100]]]])).astype('float32')
>>> scores = Tensor(np.array([[[0.2000, 0.7000, 0.1000], [0.1000, 0.8000, 0.1000], [0.3000, 0.6000, 0.1000],
...                            [0.0500, 0.9000, 0.0500]]])).astype('float32')
>>> max_output_size_per_class = Tensor(4, mstype.int32)
>>> max_total_size = Tensor(1, mstype.int32)
>>> iou_threshold = Tensor(0, mstype.float32)
>>> score_threshold = Tensor(0, mstype.float32)
>>> net = ops.CombinedNonMaxSuppression()
>>> out = net(boxes, scores, max_output_size_per_class, max_total_size, iou_threshold, score_threshold)
>>> print(out)
(Tensor(shape=[1, 1, 4], dtype=Float32, value= [[[1.00000000e+00, 1.00000000e+00, 1.00000000e+00,
                                                  1.00000000e+00]]]),
Tensor(shape=[1, 1], dtype=Float32, value= [[ 8.99999976e-01]]),
Tensor(shape=[1, 1], dtype=Float32, value= [[ 1.00000000e+00]]),
Tensor(shape=[1], dtype=Int32, value= [1]))